DDoS Detection System: Utilizing Gradient Boosting Algorithm and Apache Spark
Bibliographic record
Abstract
Distributed Denial of Service (DDoS) is one of the major threats to the Internet security. Various DDoS attacks have been reported against many organizations in recent years. There have been numerous studies investigating the effects of utilizing classification algorithms to detect and prevent DDoS attacks. However, the existing research has many obstacles including the achievement of practical performance rates of the detection system, the delay of detection, as well as the ability to deal with the large dataset. In this research, we propose a DDoS detection framework that mainly consists of Gradient Boosting classification algorithm (GBT) and the Apache Processing Engine Spark. Experimental results conducted in a Spark and Hadoop cluster, for evaluating the proposed framework regarding the performances as well as the delays using a real DDoS Dataset, show that the integration of the GBT algorithm with Apache Spark works excellently to detect DDoS attack. The volume of the dataset and the features space, as well as the depth of decision trees and number of iterations parameters, have a direct impact on the GBT algorithm performance rates and the delays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".